Although current practice focuses largely on the detection and management of obstructive coronary artery disease (CAD), there is marked heterogeneity in the relationship between CAD and risk for cardiovascular (CV) events. Some patients, despite having obstructive CAD, do not experience cardiovascular events, while others with non-obstructive CAD do. The mechanisms of this high risk coronary plaque (HRCP) development are incompletely understood; application of emerging imaging and molecular technologies holds great promise for simultaneously identifying underlying biological pathways, markers for early noninvasive detection, and novel therapeutic targets for HRCP. Thus, we propose to (1) determine the role of inflammation and other candidate biological pathways in the pathophysiology of HRCP; (2) identify novel biological pathways mediating development of HRCP through machine learning analyses of integrated metabolomic, proteomic and transcriptomic profiling; and (3) determine the incremental prognostic value of these imaging and molecular biomarkers over clinical factors and create an integrated clinico-molecular model of CV event risk prediction. We will accomplish these goals by, for the first time, integrating advanced CTA-based phenotyping of HRCP with molecular profiling and adjudicated CV events, leveraging the robust, existing resources of a large, unique NIH-funded clinical trial of imaging in chest pain patients (PROMISE). This proposal holds great public health significance by augmenting current population- and ischemia- based approaches and more precisely and preemptively identifying those patients at highest risk. Our findings will provide a critical foundation for the development of diagnostics and therapeutics targeted at specific, culprit atherosclerotic phenotypes and molecular pathways.